Distilbert-base-uncased-finetuned-combinedmodel1-ner Open-source Model - Free and Accurate Named Entity Recognition

Distilbert Base Uncased Finetuned Combinedmodel1 Ner

Developed by akshaychaudhary

This model is a fine-tuned version of distilbert-base-uncased on a specific dataset, primarily designed for Named Entity Recognition (NER) tasks.

Sequence Labeling

Transformers

Open Source License:Apache-2.0 #Lightweight BERT Fine-tuning #Named Entity Recognition #Low-resource Optimization

Downloads 15

Release Time : 3/9/2022

Model Overview

This model is a fine-tuned version of distilbert-base-uncased, suitable for Named Entity Recognition tasks. It shows moderate performance on the evaluation set, with an F1 score of 0.0481 and an accuracy of 0.7058.

Model Features

Lightweight Model

Based on the DistilBERT architecture, it is more lightweight than the original BERT model, making it suitable for resource-constrained environments.

Named Entity Recognition

Specifically designed to identify named entities in text, such as person names, locations, and organization names.

Model Capabilities

Named Entity Recognition

Text Analysis

Use Cases

Information Extraction

News Entity Recognition

Extract entities such as person names, locations, and organization names from news texts.

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	312	1.5290	0.0431	0.2278	0.0725	0.6990
0.1106	2.0	624	2.0923	0.0341	0.1722	0.0569	0.7041
0.1106	3.0	936	2.3126	0.0289	0.1443	0.0481	0.7058

Property	Details
Model Type	distilbert-base-uncased-finetuned-combinedmodel1-ner
Metrics	precision, recall, f1, accuracy

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Distilbert Base Uncased Finetuned Combinedmodel1 Ner

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